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suda1927406040 authored Oct 6, 2024
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</ul>
<div class="user_option">
<a href="htmls/member.html">
<img src="htmls/images/businessman.svg" alt="" style="height: 25px;">
<img src="htmls/images/user.png" alt="" style="height: 25px;">
</a>
<!-- <form class="form-inline my-2 my-lg-0 ml-0 ml-lg-4 mb-3 mb-lg-0">
<button class="btn my-2 my-sm-0 nav_search-btn" type="submit"></button>
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Security of LM4Code
</h1>
<p>
Deep learning techniques have revolutionized software engineering (SE), especially code-related tasks involving programming language understanding and generation. Deep neural models for code-related tasks
are collectively referred to as Language Models for Code (LM4Code) or Neural Code Models (NCMs). LM4Code demonstrates advantages in numerous
code-related tasks that traditional methods and machine learning approaches cannot rival. Although LM4Code
has demonstrated great potential in solving code-related tasks, their strong performance has also recently
&nbsp;&nbsp;&nbsp;&nbsp;Deep learning techniques have revolutionized software engineering (SE), especially code-related tasks involving programming language understanding and generation. Deep neural models for code-related tasks
are collectively referred to as neural code models (NCMs). NCMs demonstrate advantages in numerous
code-related tasks that traditional methods and machine learning approaches cannot rival. Although NCMs
have demonstrated great potential in solving code-related tasks, their strong performance has also recently
attracted the attention of (malicious) attackers from the SE and security communities.
</p>
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Security of LM4Code
</h1>
<p>
In essence, the nature
and architecture of LM4Code are also neural networks, so they also inherit the vulnerability of neural networks.
The security of LM4Code has become a focal point, with a growing number of methods proposed for attacks
and defenses against LM4Code. However, a systematic review of these attacks and defenses is currently lacking, hindering future researchers from understanding the research status, development trends, challenges,
and opportunities in the field of LM4Code security.
&nbsp;&nbsp;&nbsp;&nbsp;In essence, the nature
and architecture of NCMs are also neural networks, so they also inherit the vulnerability of neural networks.
The security of NCMs has become a focal point, with a growing number of methods proposed for attacks
and defenses against NCMs. However, a systematic review of these attacks and defenses is currently lacking, hindering future researchers from understanding the research status, development trends, challenges,
and opportunities in the field of NCM security.
</p>
<!-- <div class="">
<a href="">
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Security of LM4Code
</h1>
<p>
In this paper, we present a comprehensive and systematic
survey of research on LM4Code security. Specifically, we first employ a systematic approach to retrieve relevant
&nbsp;&nbsp;&nbsp;&nbsp;In this paper, we present a comprehensive and systematic
survey of research on NCM security. Specifically, we first employ a systematic approach to retrieve relevant
research literature from the SE, security, and artificial intelligence (AI) communities, and finally carefully select
representative papers from an initial pool of candidates. Second, after reviewing
these papers, we provide an overview of the security threats faced by LM4Code and categorize these threats
these papers, we provide an overview of the security threats faced by NCMs and categorize these threats
into backdoor attacks, adversarial attacks, and other attacks based on the methods of their implementation.
Third, for each category, we depict the general workflow and summarize existing attacks as well as defenses.
Moreover, we also scrutinize the widely used datasets, evaluation metrics, and replication packages utilized in
LM4Code security studies. Finally, we outline key challenges and potential future research opportunities, aiming
to provide useful guidance for subsequent researchers to further advance the security of LM4Code.
NCM security studies. Finally, we outline key challenges and potential future research opportunities, aiming
to provide useful guidance for subsequent researchers to further advance the security of NCMs.
</p>
<!-- <div class="">
<a href="">
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<h2>
Paper Classification
</h2>
<p>
三个分类的总体介绍
</p>
</div>
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<div class="item">
<div class="box">
<div class="img-box">
<img src="htmls/images/recent_papers/shield.png" alt="">
<a href="https://arxiv.org/pdf/2304.13255"><img src="htmls/images/recent_papers/shield.png" alt=""></a>
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<a href="https://arxiv.org/pdf/2301.02496"><img src="htmls/images/recent_papers/yangzhou.png" alt=""></a>
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<img src="htmls/images/recent_papers/CODA.png" alt="">
<a href="https://arxiv.org/pdf/2301.02412"><img src="htmls/images/recent_papers/CODA.png" alt=""></a>
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